code: def __init__(self, gpt2, num_labels):
      super().__init__()
      self.gpt2 = gpt2
      vocab_size = self.gpt2.vocab_size
      self.linear = nn.Linear(vocab_size, num_labels)
      self.logit = nn.Sigmoid()
      self.criterion = nn.BCELoss()
      self.train_f1 = F1(mdmc_average="global")
      self.valid_f1 = F1(mdmc_average="global")
  
file_path: underthesea/examples/sentiment/train_gpt2.py
imports: import hydra
  import pytorch_lightning
  from omegaconf import DictConfig
  from torch.optim import SGD
  import torch.nn
  from torchmetrics import F1Score
  from transformers import AutoTokenizer, AutoModelWithLMHead
  from pytorch_lightning.loggers import WandbLogger
  from examples.sentiment.data import MultiLabelClassificationDatamodule
  from underthesea.datasets.uit_absa_hotel.uit_absa_hotel import UITABSAHotel
label_desc: Use image node_id 1 to create a new GPT2TextClassification object from inherited base classes: pl with example: obj = GPT2TextClassification(gpt2, num_labels)